Marketing communications (Marcom) is a business area in connection with which there is often little confidence in a clear nexus between spending and results. This lack of confidence arises because these communications create an environment in which sales are generated, but only rarely can be directly linked with actual marketing initiative. Although it is possible to generate data that shows a trending relationship between marcom spending and sales, with aggregated results, the impact of each individual marketing decision is rarely measurable, making it impossible to forecast critical aspects of marketing communication.
Accordingly, a need has been clearly identified in the industry for an analytic solution that can be used by marketing departments and corporate support to define and improve marketing communication costs and contribution to an organization, and more accurately forecast spending, as well as impact on sales channels and results.
Although most organizations collect and maintain vast amounts of data concerning market spending and sales, much of the formal data is administered by IT and therefore kept secure, while an equally huge volume of information is maintained on desktop computers and in file drawers. Corporate policies rarely mandate the sharing of such data, and there is no known method for unifying and interpreting the data. When organizations do decide to analyze the correlation between marketing spend and sales, they almost always consider only the end results in terms of ‘bottom line’ accounting figures, rather than wading through the volume of data that describes the complicated marketing mix of middle stages and steps. However, it is in the realities of this marketing mix, with varying degrees of proficiency and knowledge of sales cycles that almost all analytic solutions fail.
Moreover, upon considering and analyzing marketing and sales data, it is often difficult to isolate the conditions that influence success from among the multiplicity of obvious factors such as budget, branding, timing of tactics and competitive activity, as well as more subtle influences such as weather and interest rates. Given this complexity, it is impossible to use the data to run useful scenarios or forecasts for virtually all but the most structured and sophisticated data driven organizations.
Most organizations invest heavily in customer information (CRM), on the reasonable assumption that improved knowledge of each individual consumer will improve service and retention. However, there is a tendency to overweight and overwork data from the CRM base for planning purposes. Hence, the selection of target customers and the implementation of one-to-one strategies have occupied the innovation space in marketing since the late '80's.
Direct Marketing (DM) has also developed in conjunction with CRM, and is characterized by the ability to directly link sales to elements of the marketing spend. Proponents of DM have claimed full credit for specific sales success, thereby derogating other forms of marketing. For some organizations, the shift to DM strategies has been based solely on the ability to represent some form of return on investment (R.O.I). At the same time, the emergence of DM has begun to blur the lines between sales and marketing, resulting in new rules concerning ‘leads’ and conversion, and new challenges for the marketing industry.
Although CRM can provide targeting information, it remains silent on assessing the various tools of marketing communications (marcom) performance. The current state of the marketing and sales environment tends towards a silo-like structure. Within each silo, there is a limited view of performance. Each relies on its own data to deliver, plan, manage expectations, and develop an R.O.I. ‘story’. However, these R.O.I. stories can rarely be compared or aggregated.
Until recently, software supported marketing analytic platforms were rare and typically only implemented as custom environment analysts, which are expensive, mostly statistical, and unable to do scenario planning at a useful speed. Consequently, such systems have proven to be inadequate for traditional businesses with multiple brands and lines of business and sales channels.
Of those businesses currently employing marketing analytics, each either uses customer-centric data as the base for trend analysis, or an analytic process to combine CRM and econometric data for trend analysis, or perform media metrics to manage opportunity strictly in the media mix. Simple media metrics tend to exhaust their R.O.I benefits in a short window, and both traditional CRM based and media metrics models cannot integrate widely diverse marketing communication investments. More particularly, although CRM is currently used to carefully consider markets, targets, offers and vehicles and link these scenarios to results, the CRM solutions that have proven to work are virtually unrepeatable because the circumstances of the interaction and the conditions of performance are not known or repeatable.